The field of color management has evolved past its three-channel (i.e., red-green-blue, or RGB) device and appearance foundations into more accurate and flexible solutions, such as spectral imaging. Generally, spectral imaging is defined as the acquisition, processing, display and/or interpretation of images with a high number of (i.e., greater than three) spectral channels. The full range or spectrum of colors recognized and/or reproducible by any particular color system is referred to as the “gamut” of that system. Such a gamut is also sometimes considered in terms of a sub-region within a greater “color space”.
Conversion or translation between the gamut of one device (or system) and another is referred to as “gamut mapping”. Gamut mapping is ubiquitous to countless processes such as, for example, recording an image with a digital camera, and then rendering that image on paper with a color printer.
Presently, spectral processing is embodied in either homogeneous systems that do not require gamut mapping, or that incorporate simplistic assumptions that are hard coded into the system or associated device. Historical “clipping” device RGB algorithms are similar in this regard. A third alternative of spectral processing is a very iterative approach in which simple spectral metameric matches are made to attempt to minimize a color difference between entities. Such an approach is used by paint and manufacturing industries, for example, to mix numerous paint colors so as to match an existing sample such as a floor tile or cabinet surface.
Practically speaking, the light spectrum incident on a subject can vary substantially over time, resulting in an obvious change in appearance to a human observer. Furthermore, device performance remains constrained by the gamut of that particular device. Modern systems seek to use spectral processing to ensure accurate reproductions across widely varying rendering and viewing conditions. There is a continual effort to improve overall color performance of devices and systems, particularly in the field of gamut mapping.